scholarly journals Comparative Analysis between Naïve Bayes Algorithm and Decision Tree to Solve WSD Using Empirical Approach

2016 ◽  
Vol 4 (1) ◽  
pp. 82-86 ◽  
Author(s):  
Boshra F. Zopon Al-Bayaty ◽  
◽  
Shashank Joshi ◽  
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Empirical Approach ◽  
Bayes Algorithm

scholarly journals Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms

2018 ◽  
Vol 7 (1.7) ◽  
pp. 137 ◽  
Author(s):  
Danda Shashank Reddy ◽  
Chinta Naga Harshitha ◽  
Carmel Mary Belinda
Keyword(s):  
Brain Tumor ◽  
Decision Tree ◽  
Naive Bayes ◽  
Classification Problem ◽  
Naïve Bayes ◽  
World Health ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Computer Tomography Scan ◽  
Bayes Algorithm

Now a day’s many advanced techniques are proposed in diagnosing the tumor in brain like magnetic resonance imaging, computer tomography scan, angiogram, spinal tap and biospy. Based on diagnosis it is easy to predict treatment. All of the types of brain tumor are officially reclassified by the World Health Organization. Brain tumors are of 120 types, almost each tumor is having same symptoms and it is difficult to predict treatment. For this regard we are proposing more accurate and efficient algorithm in predicting the type of brain tumor is Naïve Bayes’ classification and decision tree algorithm. The main focus is on solving tumor classification problem using these algorithms. Here the main goal is to show that the prediction through the decision tree algorithm is simple and easy than the Naïve Bayes’ algorithm.

scholarly journals Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2067
Author(s):  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs ◽  
João Gilberto Mendes dos Reis ◽  
Raquel Baracat Tosi Rodrigues da Silva
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Rating Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Environmental Efficiency ◽  
Classification Model ◽  
Bayes Algorithm ◽  
J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

scholarly journals ANALISIS KINERJA ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MEMPREDIKSI PRESTASI SISWA SEKOLAH MENENGAH KEJURUAN

2019 ◽  
Vol 5 (1) ◽  
pp. 23-28
Author(s):  
Astrid Noviriandini ◽  
Nurajijah Nurajijah
Keyword(s):  
Student Achievement ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Tree Model ◽  
Learning Achievement ◽  
Achievement Prediction ◽  
C4.5 Decision Tree ◽  
Auc Value ◽  
Bayes Algorithm

This research informs students and teachers to anticipate early in following the learning period in order to get maximum learning outcomes. The method used is C4.5 decision tree algorithm and Naïve Bayes algorithm. The purpose of this study was to compare and evaluate the decision tree model C4.5 as the selected algorithm and Naïve Bayes to find out algorithms that have higher accuracy in predicting student achievement. Learning achievement can be measured by the value of report cards. After comparison of the two algorithms, the results of the learning achievement prediction are obtained. The results showed that the Naïve Bayes algorithm had an accuracy value of 95.67% and the AUC value of 0.999 was included in Excellent Clasification, for the C4.5 algorithm the accuracy value was 90.91% and the AUC value of 0.639 was included in the state of Poor Clasification. Thus the Naïve Bayes algorithm can better predict student achievement.

scholarly journals ANALISA PERBANDINGAN ALGORITMA K-MEANS, DECISION TREE, DAN NAE BAYES UNTUK SISTEM PENGELOMPOKKAN SISWA OTOMATIS

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Oktariani Nurul Pratiwi
Keyword(s):  
Comparative Analysis ◽  
Cooperative Learning ◽  
Decision Tree ◽  
Student Teachers ◽  
Naive Bayes ◽  
Group Learning ◽  
Naïve Bayes ◽  
Long Time ◽  
Automatic Grouping

[INA]Pembelajaran kooperatif adalah proses pembelajaran dengan mengikuti beberapa instruksi yang melibatkan siswa bekerja dalam tim untuk mencapai tujuan bersama dalam kondisi yang mencakup unsur-unsur yang telah ditentukan. Pembelajaran kooperatif mampu membuat siswa lebih percaya diri dan mampu mengungkapkan pendapat sehingga, dapat meningkatkan kemampuan siswa lebih efektif. Salah satu faktor pendukung pembelajaran kooperatif adalah komposisi siswa dalam kelompok. Sebaiknya, penentuan kelompok belajar di sekolah dilakukan berdasarkan pengamatan guru terhadap siswa. Namun hal ini tidak mudah karena membutuhkan waktu lama, dan membuat beban kerja guru bertambah. Sehingga, cara termudah penentuan kelompok belajar adalah ditentukan secara acak. Risikonya, proses pembelajaran kooperatif tidak berjalan dengan efektif. Dibutuhkan sebuah sistem yang mampu menentukan komposisi anggota belajar siswa secara otomatis. Dalam paper ini dipaparkan hasil analisis perbandingan algoritma K-means, Decision Tree, Naive Bayes terhadap data siswa yang dapat digunakan untuk pengelompokkan siswa. Dari hasil uji coba didapatkan Nae Bayes mampu mengelompokkan siswa lebih baik dengan nilai akurasi 70,37%.Kata kunci :Pembelajaran Kooperatif, Pengelompokkan Siswa Otomatis, K-Means, Decision Tree, Nae Bayes.[EN]Cooperative learning is a process of learning that following some instructions involving students work in teams to achieve goal in a condition. Cooperative learning makes students more confident to tell opinions, and improve the ability of students more effectively. Formation of students in a group is important. Preferably, the determination of group learning in school is based on the observation of the student teachers. But, it is not easy because it takes a long time, and increase teacher task. The easiest way of determining groups of students by determined randomly. Hence, cooperative learning become inefficient. This process need a system that capable to determine the formation of the student's learning automatically. In this paper presented the results of a comparative analysis of K-means algorithm, Decision Tree, Nae Bayes. The best result of the experiment by Nae Bayes 70,37% accurately.Keywords : Cooperative Learning, Automatic Grouping of Student, K-means, Decision Tree, Nae Bayes.

scholarly journals A COMPARISON OF ACCURACY BETWEEN TWO METHODS: NAЇVE BAYES ALGORITHM AND DECISION TREE-J48 TO PREDICT THE STOCK PRICE OF PT ASTRA INTERNATIONAL Tbk USING DATA FROM INDONESIA STOCK EXCHANGE

2019 ◽  
Vol 7 (1) ◽  
pp. 1244-1258
Author(s):  
Joan Yuliana Hutapea ◽  
Yusran Timur Samuel ◽  
Heima Sitorus
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Stock Prices ◽  
Stock Price ◽  
Naive Bayes ◽  
Stock Exchange ◽  
Naïve Bayes ◽  
Bayes Method ◽  
Testing Data ◽  
Bayes Algorithm

The ability to predict the stock prices is very important for market players, whether individual or organizational investors.  The market players needs to know how to predict, that will help them in their decision making process, whether to buy or to sell its shares, so that it can maximize profits and reduce potential losses due to mistakes in decision making.  In accordance to this, the authors conducted a study that aimed to analyze and to compare the accuracy of two (2) methods that is used to predict the stock prices, namely: the Naїve Bayes Method and the Decision Tree-J48 Method. The amount of data used in this study were 1,195 stock datas of PT Astra International Tbk, issued by the IDX, by the period of January 1, 2013 to November 30, 2017. This study uses 7 attributes, namely:  Previews, High, Low, Close, Volume, Value, and Frequency. By using the WEKA application the result shows that, the accuracy of the Naïve Bayes Method using 20% of testing data, is 92.0502%, the precision value is 0.920 and the value of recall is 0.961,  while the accuracy of the Decision Tree J-48 method, using 20% of testing data, is 98.7448%, with precision value of 0.989 and the value of recall of 0.997.   Through this results,  it can be concluded that the decision tree J-48 algorithm has a better accuracy results compared to the Naive Bayes algorithm in predicting the stock price of PT. Astra Internasional Tbk.

scholarly journals ICU Patient Prediction for Moving with Decision Tree C4.5 and Naïve Bayes Algorithm

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 88 ◽  
Author(s):  
Sumpena Sumpena ◽  
Yuma Akbar ◽  
Nirat Nirat ◽  
Mario Hengky
Keyword(s):  
Intensive Care ◽  
Decision Tree ◽  
Medical Information ◽  
Naive Bayes ◽  
Venous Pressure ◽  
Naïve Bayes ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Sepsis Diagnosis ◽  
Bayes Algorithm

Critical patients need intensive care and supervision by the medical team in the Intensive Care Unit (ICU), including ventilators, monitors, Central Venous Pressure (CVP), Electrocardiogram (ECG), Echocardiogram (ECHO), medical supply, and medical information that is fast, precise, and accurate. In the ICU treatment room requires data that needs to be processed and analyzed for decision making. This study analyzed the ventilator, CVP and also Sepsis Diagnosis related to the data of moving patients and patients dying. This study also uses the decision tree algorithm C.45 and Naive Bayes to determine the level of accuracy of patient care and supervision information in the ICU. The results showed that the decision tree algorithm C.45 has an accuracy of 81.55% and Naive Bayes of 81.54%. The decision tree C.45 algorithm has almost the same advantages as the Naive Bayes algorithm.

scholarly journals Tweet Netizen Prediction Using Random Forest, Decision Tree, Naïve Bayes, And Ensemble Algorithm (Case Study The Governor Of DKI Jakarta)

SinkrOn ◽  
2020 ◽  
Vol 5 (1) ◽  
pp. 9-20
Author(s):  
Antonius Yadi Kuntoro
Keyword(s):  
Social Media ◽  
Random Forest ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Testing Data ◽  
Negative Comments ◽  
Ensemble Algorithm ◽  
Bayes Algorithm

Abstract — The current Governor of DKI Jakarta, even though he has been elected since 2017 is always interesting to talk about or even comment on. Comments that appear come from the media directly or through social media. Twitter has become one of the social media that is often used as a media to comment on elected governors and can even become a trending topic on Twitter social media. Netizens who comment are also varied, some are always Tweeting criticism, some are commenting Positively, and some are only re-Tweeting. In this research, a prediction of whether active Netizens will tend to always lead to Positive or Negative comments will be carried out in this study. Model algorithms used are Decision Tree, Naïve Bayes, Random Forest, and also Ensemble. Twitter data that is processed must go through preprocessing first before proceeding using Rapidminer. In trials using Rapidminer conducted in four trials by dividing into two parts, namely testing data and training data. Comparisons made are 10% testing data: 90% Training data, then 20% testing data: 80% training data, then 30% testing data: 70% training data, and the last is 35% testing data: 65% training data. The average Accuracy for the Decision Tree algorithm is 93.15%, while for the Naïve Bayes algorithm the Accuracy is 91.55%, then for the Random Forest algorithm is 93.41, and the last is the Ensemble algorithm with an Accuracy of 93, 42%. here. Keywords — Decision Tree, Naïve Bayes, Random Forest, Set, Twitter.  

scholarly journals Multiclass Software Bug Severity Classification using Decision Tree, Naive Bayes and Bagging

2021 ◽  
Vol 12 (2) ◽  
pp. 1859-1865
Author(s):  
Raj Kumar, Et. al.
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Software Development ◽  
Development Process ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Software Development Process ◽  
Software Bugs ◽  
Software Applications ◽  
Severity Classification

The software applications are experiencing the challenges of ever-growing complexity caused by the increase in the number of bugs. The software development process has been adversely affected due to the wastage of resources caused due to the bugs. It is imperative to identify and predict bugs to facilitate the software development process. Software bugs can be classified according to the severity of the bugs. In this paper a comparative analysis of Decision Tree, Naïve Bayes and Bagging approach is done for the bug severity classification. A comparative analysis of the Naïve Bayes, Decision Tree and Bagging approach is done for the accuracy, precision, recall and F-measure parameters

scholarly journals To Identify the Improvement Pattern of Self-financing Arts and Science College Student’s Academic Performance using Classification Algorithms

2019 ◽  
Vol 8 (2) ◽  
pp. 2429-2433
Keyword(s):  
Academic Performance ◽  
Decision Tree ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Classification Algorithms ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Science College ◽  
Bayes Algorithm

The aim of this research work is to identify the improvement pattern of academic performance of final year students of self-financing arts and science colleges. The data was collected from the students of nine Arts and Science Colleges. The data contains demographic, socio-economic, residence and college location, subjects, infrastructural facilities, faculty concern and self-motivation attributes. The classification algorithms like Naïve Bayes, Decision tree and CBPANN are applied on the student’s data. The outcome of the research can be used to improve the academic performance students studying in self-financing arts and science colleges located in educationally backward areas. The experiment results shows that the accuracy value for Naïve Bayes algorithm is 92.63%, accuracy value for Decision Tree algorithm is 96.41% and accuracy value for CBPANN algorithm is 99.49%

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